ArticleEuropean radiology2025
Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning for predicting muscle loss after radiotherapy using clinical and toxicity data in oral cavity cancer.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Performance of Machine Learning Models for Prognosis Prediction in Oral Cavity Squamous Cell Carcinoma: A Systematic Review.Cancers · 2026Review
- Radiomics-based unsupervised clustering and deep learning nomogram for response prediction after neoadjuvant chemotherapy in locally advanced laryngeal cancer.BMC medical imaging · 2026Article
- Predictors of aspiration pneumonia in oral cavity cancer patients undergoing radiation therapy: A focus on skeletal muscle index.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- A predictive clinical-radiomics nomogram for early diagnosis of mesenteric arterial embolism based on non-contrast CT and biomarkers.Abdominal radiology (New York) · 2025Article
- Androgen Deprivation Therapy-Induced Muscle Loss and Fat Gain Predict Cardiovascular Events in Prostate Cancer Patients.Journal of cachexia, sarcopenia and muscle · 2025Article
- Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.Journal of cellular and molecular medicine · 2025Article
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7 authors.
Funding
Abstract
objectivesMuscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial intelligence to identify the threshold of muscle loss associated with survival in OCSCC. MATERIALS AND
methodsWe enrolled 1087 patients with OCSCC treated with surgery and adjuvant radiotherapy at two tertiary centers (660 in the derivation cohort and 427 in the external validation cohort). Skeletal muscle index (SMI) was measured using pre- and post-radiotherapy computed tomography (CT) at the C3 vertebral level. Random forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) models were developed to predict all-cause mortality, and their performances were evaluated using the area under the curve (AUC). Muscle loss threshold was identified using the SHapley Additive exPlanations (SHAP) method and validated using Cox regression analysis.
resultsIn the external validation cohort, the RF, XGBoost, and CatBoost models achieved favorable performance in predicting all-cause mortality (AUC: 0.898, 0.859, and 0.842). The SHAP method demonstrated that SMI change after radiotherapy was the most important feature for predicting all-cause mortality and consistently identified SMI loss ≥ 4.2% as the threshold in all three models. In multivariable analysis, SMI loss ≥ 4.2% was independently associated with increased all-cause mortality risk in both cohorts (derivation cohort: hazard ratio: 6.66, p < 0.001; external validation cohort: hazard ratio: 8.46, p < 0.001).
conclusionThis study can assist clinicians in identifying patients with considerable muscle loss after treatment and guide interventions to improve muscle mass. KEY POINTS: Question Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity cancer; however, the threshold of muscle loss remains unclear. Findings Explainable artificial intelligence identified muscle loss ≥ 4.2% as the threshold of increased all-cause mortality risk in both derivation and external validation cohorts. Clinical Relevance Muscle loss ≥ 4.2% may be the optimal threshold for survival in patients who receive adjuvant radiotherapy for oral cavity cancer. This threshold can guide clinicians in improving muscle mass after radiotherapy.
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